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In this chapter, we outline a structured approach that guides organizations through three key stages of AI use case development. First, we focus on the identification of promising use cases by combining organizational needs with AI capabilities. Next, we address the design of AI-based services using the AI Service Canvas, which helps structure data requirements, business potential, and organizational integration. Finally, we present the evaluation of AI use cases through the effect-path model—a tool for systematically tracing how data and AI capabilities generate business value and competitive advantage.

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Identifying, Designing, and Evaluating AI Use Cases

  • 章节

  • 发表日期：

    2026年

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  • Nils Urbach,
  • Daniel Feulner,
  • Simon Feulner,
  • Dominik Protschky

摘要

In this chapter, we outline a structured approach that guides organizations through three key stages of AI use case development. First, we focus on the identification of promising use cases by combining organizational needs with AI capabilities. Next, we address the design of AI-based services using the AI Service Canvas, which helps structure data requirements, business potential, and organizational integration. Finally, we present the evaluation of AI use cases through the effect-path model—a tool for systematically tracing how data and AI capabilities generate business value and competitive advantage.